Identification of Motor Unit Twitch Properties in the Intact Human In Vivo

Restoring natural motor function in neurologically injured individuals is challenging, largely due to the lack of personalization in current neurorehabilitation technologies. Signal-driven neuro-musculoskeletal models may offer a novel paradigm for devising novel closed-loop rehabilitation strategies according to an individual's physiology. However, current modelling techniques are constrained to bipolar electromyography (EMG), thereby lacking the resolution necessary to extract the activity of individual motor units (MUs) in vivo. In this work, we decoded MU spike trains from high-density (HD)-EMG to obtain relevant neural properties across multiple isometric plantar-dorsiflexion tasks. Then, we sampled MU statistical distributions and used them to reproduce MU specific activation profiles. Results showed bimodal distributions which may correspond to slow and fast MU populations. The estimated activation profiles showed a high degree of similarity to the reference torque (R2>0.8) across the recorded muscles. This suggests that the estimation of MU twitch properties is a crucial step for the translation of neural information into muscle force.Clinical Relevance- This work has multiple implications for understanding the underlying mechanism of motor impairment and for developing closed-loop strategies for modulating alpha motor circuitries in neurologically injured individuals

Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors

Background: Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data. Methods: Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal–Wallis test and Dunn post hoc analysis. Results: Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly (p &lt; 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00–01:00), and in step count, more at measurement days 6 and 7 (p &lt; 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization. Conclusions: Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.</p

The new technique for accurate estimation of the spinal cord circuitry:recording reflex responses of large motor unit populations

We propose and validate a non-invasive method that enables accurate detection of the discharge times of a relatively large number of motor units during excitatory and inhibitory reflex stimulations. HDsEMG and intramuscular EMG (iEMG) were recorded from the tibialis anterior muscle during ankle dorsiflexions performed at 5%, 10%, and 20% of the maximum voluntary contraction (MVC) force, in 9 healthy subjects. The tibial nerve (inhibitory reflex) and the peroneal nerve (excitatory reflex) were stimulated with constant current stimuli. In total, 416 motor units were identified from the automatic decomposition of the HDsEMG. The iEMG was decomposed using a state-of-the-art decomposition tool and provided 84 motor units (average of two recording sites). The reflex responses of the detected motor units were analyzed using the peri-stimulus time histogram (PSTH) and the peri-stimulus frequencygram (PSF). The reflex responses of the common motor units identified concurrently from the HDsEMG and the iEMG signals showed an average disagreement (the difference between number of observed spikes in each bin relative to the mean) of 8.2±2.2% (5% MVC), 6.8±1.0% (10% MVC), and 7.5±2.2% (20% MVC), for reflex inhibition, and 6.5±4.1%, 12.0±1.8%, 13.9±2.4%, for reflex excitation. There was no significant difference between the characteristics of the reflex responses, such as latency, amplitude and duration, for the motor units identified by both techniques. Finally, reflex responses could be identified at higher force (four of the nine subjects performed contraction up to 50% MVC) using HDsEMG but not iEMG, because of the difficulty in decomposing the iEMG at high forces. In conclusion, single motor unit reflex responses can be estimated accurately and non-invasively in relatively large populations of motor units using HDsEMG. This non-invasive approach may enable a more thorough investigation of the synaptic input distribution on active motor units at various force levels

Muscle contractile properties directly influence shared synaptic inputs to spinal motor neurons

Alpha band oscillations in shared synaptic inputs to the alpha motor neuron pool can be considered an involuntary source of noise that hinders precise voluntary force production. This study investigated the impact of changing muscle length on the shared synaptic oscillations to spinal motor neurons, particularly in the physiological tremor band. Fourteen healthy individuals performed low-level dorsiflexion contractions at ankle joint angles of 90° and 130°, while high-density surface electromyography (HDsEMG) was recorded from the tibialis anterior (TA). We decomposed the HDsEMG into motor units spike trains and calculated the motor units’ coherence within the delta (1–5 Hz), alpha (5–15 Hz), and beta (15–35 Hz) bands. Additionally, force steadiness and force spectral power within the tremor band were quantified. Results showed no significant differences in force steadiness between 90° and 130°. In contrast, alpha band oscillations in both synaptic inputs and force output decreased as the length of the TA was moved from shorter (90°) to longer (130°), with no changes in delta and beta bands. In a second set of experiments (10 participants), evoked twitches were recorded with the ankle joint at 90° and 130°, revealing longer twitch durations in the longer TA muscle length condition compared to the shorter. These experimental results, supported by a simple computational simulation, suggest that increasing muscle length enhances the muscle's low-pass filtering properties, influencing the oscillations generated by the Ia afferent feedback loop. Therefore, this study provides valuable insights into the interplay between muscle biomechanics and neural oscillations

Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.Peer ReviewedPostprint (published version

Effect of gender, age, fatigue and contraction level on electromechanical delay

WOS: 000281435800011PubMed ID: 20430696Objective: The aim of this study was to determine electromechanical delay (EMD) using supramaximal stimuli and to investigate its variation with gender, age, contraction level and fatigue. Methods: Fifteen male and 15 female healthy subjects (aged between 18 and 60) participated in our study. Electromyogram (EMG) recordings were taken from triceps surae muscle. While subjects contracted their muscles voluntarily at specified percentages of maximum voluntary contraction, 10 supramaximal stimuli were applied to the tibial nerve. The time lag between the onset of the EMG response (M-wave) and the onset of force generation was calculated as EMD. Results: EMD was found to be 8.5 +/- 1.3 ms (at rest condition), which is much shorter than those reported in previous studies. Although EMD did not significantly vary with gender (P > 0.05), it decreased significantly with escalating muscle contraction level (P < 0.05) and increased significantly with advancing age and with fatigue (P < 0.05). Conclusions: EMD was found to be considerably shorter than those reported in previous studies, and hence we discuss the possible reasons underlying this difference. We suggest that supramaximal nerve stimulation and high resolution EMG and force recording may have generated this difference. Significance: Current findings suggest that EMD is very sensitive to the method used to determine it. We discuss the reasons for the short EMD value that we have found in the present study. (C) 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.Marie Curie ChairEuropean Union (EU) [MEX-CT-2006-040317]; Turkish Scientific and Technological Research OrganizationTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [TUBITAK - 107S029 - SBAG-3556]This study is supported by the Marie Curie Chair project (GenderReflex; MEX-CT-2006-040317) and Turkish Scientific and Technological Research Organization (TUBITAK - 107S029 - SBAG-3556). We would like to thank Ms. Merve Ulug. for the diagram in Fig. 1

Cutaneous silent period in human FDI motor units

WOS: 000281319500003PubMed ID: 20694723In this study, we aimed to use both the probability-based and the frequency-based analyses methods simultaneously to examine cutaneous silent period (CSP) induced by strong electrical currents. Subjects were asked to contract their first dorsal interosseus muscles so that one motor unit monitored via intramuscular wire electrodes discharged at a rate of approximately 8 Hz. Strong electrical stimuli were delivered to the back of the hand that created a subjective discomfort level of between 4 and 7 [0-10 visual analogue scale] and induced cutaneous silent period in all units. It was found that the duration of the CSP was significantly longer when the same data were analysed using frequency-based analysis method compared with the probability-based methods. Frequency-based analysis indicated that the strong electrical stimuli induce longer lasting inhibitory currents than what was indicated using the probability-based analyses such as surface electromyogram and peristimulus time histogram. Usage of frequency-based analysis for bringing out the synaptic activity underlying CSP seems essential as its characteristics have been subject to a large number of studies in experimental and clinical settings.Marie Curie Chair projectEuropean Union (EU) [MEX-CT-2006-040317]; Turkish Scientific and Technological Research OrganizationTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [TUBITAK-107S029-SBAG-3556]This study is supported by the Marie Curie Chair project (GenderReflex; MEX-CT-2006-040317) and Turkish Scientific and Technological Research Organization (TUBITAK-107S029-SBAG-3556). We wish to thank Professor Gurbuz Celebi for reading the first draft and advice

Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors

Background: Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data. Methods: Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal–Wallis test and Dunn post hoc analysis. Results: Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly (p p < 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization. Conclusions: Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data